CN116977925B - Video safety management system for omnibearing intelligent monitoring - Google Patents
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 22
- 238000001514 detection method Methods 0.000 claims abstract description 30
- 238000004458 analytical method Methods 0.000 claims abstract description 21
- 238000007689 inspection Methods 0.000 claims abstract description 12
- 230000000391 smoking effect Effects 0.000 claims abstract description 12
- 238000011897 real-time detection Methods 0.000 claims abstract description 8
- 238000007619 statistical method Methods 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 21
- 230000002159 abnormal effect Effects 0.000 claims description 17
- 238000007726 management method Methods 0.000 claims description 17
- 238000013528 artificial neural network Methods 0.000 claims description 10
- 241000699666 Mus <mouse, genus> Species 0.000 claims description 9
- 238000010223 real-time analysis Methods 0.000 claims description 7
- 238000003062 neural network model Methods 0.000 claims description 6
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- 239000000779 smoke Substances 0.000 description 4
- 230000006399 behavior Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 235000021393 food security Nutrition 0.000 description 2
- 206010000117 Abnormal behaviour Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
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- G06V20/50—Context or environment of the image
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- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
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Abstract
The application relates to a video safety management system for omnibearing intelligent monitoring, which is characterized by comprising the following modules: the camera module is used for acquiring video or photo information of the catering center according to the setting of the user; the AI analysis module is used for carrying out real-time detection or detection analysis according to the setting of a user; the intelligent inspection module, the camera module can change the position according to the preset track to obtain more detailed video or photo information; and the monitoring management module can carry out corresponding statistical analysis according to the selection parameters of the user. The application can fully automatically analyze illegal actions such as wearing no mask, smoking, wearing no cap, using a mobile phone and the like in real time or non-real time with high precision, can intelligently identify whether mouse patients exist, and warn related personnel to ensure food safety.
Description
Technical Field
The invention belongs to the technical field of intelligent monitoring, and particularly relates to a video safety management system, a video safety management method, a video safety management device, computer equipment and a computer readable storage medium for omnibearing intelligent monitoring.
Background
The food security platform system is mainly used for monitoring each production link of a kitchen and early warning the nonstandard behavior of food production in the food security production process, and the intelligent help supervision department monitors and manages each dining hall of schools, institutions and the like in real time.
In the prior art, although the links can be monitored, intelligent analysis and real-time analysis cannot be performed, and the method can only be used for subsequent investigation and evidence collection, and for food safety production, it is necessary to be able to discover and timely process nonstandard behaviors in real time.
Disclosure of Invention
In order to solve the problems, the invention provides a video safety management system for omnibearing intelligent monitoring, which is characterized by comprising the following modules:
the camera module is used for acquiring video or photo information of the catering center according to the setting of the user;
The AI analysis module is used for carrying out real-time detection or detection analysis according to the setting of a user;
the intelligent inspection module, the camera module can change the position according to the preset track to obtain more detailed video or photo information;
and the monitoring management module is used for carrying out corresponding statistical analysis according to the selection parameters of the user.
As an embodiment, the system further comprises an intelligent guide rail for use with the camera module.
As an embodiment, the system further includes an intelligent control module, capable of sending a control instruction according to the information acquired by the camera module, so as to control the intelligent inspection module to move.
As an embodiment, the AI analysis module implements mask wear detection, cap detection, smoke detection, and mouse detection.
As an embodiment, the system further comprises an early warning module, and when the abnormal condition or the irregular condition is detected in real time, the early warning module sends out warning information to related responsible personnel.
The application also provides a video safety management method for omnibearing intelligent monitoring, which is characterized by comprising the following steps:
S1, acquiring information, namely acquiring video or photo information of a catering center according to the setting of a user;
s2, AI analysis step, real-time detection or detection analysis is carried out according to the setting of the user;
s3, an intelligent inspection step, wherein the camera module performs position change according to a preset track so as to obtain more detailed video or photo information;
and S4, monitoring and managing, namely performing corresponding statistical analysis according to the selection parameters of the user.
As an embodiment, the method further comprises: the camera module is matched with the intelligent guide rail for use, and multi-angle information acquisition is completed.
As an embodiment, the method further includes sending, by using an intelligent control module, an instruction according to the information obtained by the camera module, so as to control the intelligent inspection module to move.
As an embodiment, the AI analysis step implements mask wear detection, cap detection, smoke detection, and mouse disease detection.
As an embodiment, the method further comprises sending a warning message to the relevant responsible person by the early warning module when the nonstandard or abnormal condition is detected in real time.
Furthermore, the invention provides a computer device comprising a memory, a processor and a transceiver in communication connection in turn, wherein the memory is used for storing a computer program, the transceiver is used for receiving and transmitting messages, and the processor is used for reading the computer program and executing any one of the methods.
The present invention provides a computer readable storage medium having instructions stored thereon which, when executed on a computer, perform any of the methods described above.
The invention has the beneficial effects that:
The invention provides a video safety management system for omnibearing intelligent monitoring, which is characterized by comprising the following modules:
The camera module is used for acquiring video or photo information of the catering center according to the setting of the user; the AI analysis module is used for carrying out real-time detection or detection analysis according to the setting of a user; the intelligent inspection module, the camera module can change the position according to the preset track to obtain more detailed video or photo information; and the monitoring management module is used for carrying out corresponding statistical analysis according to the selection parameters of the user. The application realizes AI real-time monitoring and analysis of nonstandard operation behaviors of the kitchen, such as real-time monitoring and alarm intelligent timing inspection without wearing a mask, smoking and the like, and analysis of abnormal behaviors and alarm; the intelligent sample reserving, the sample reserving cabinet is opened to automatically photograph, the door is closed to automatically photograph the record, abnormality and problems can be found in time, and the food safety level is improved.
The application also relates to a special deep neural network which is specially suitable for identifying abnormal scenes in the food safety field, belongs to the original contribution of the application, and can realize real-time analysis and detection.
Drawings
FIG. 1 is a diagram showing AI real-time analysis of the invention
FIG. 2 is a diagram showing the intelligent patrol record according to the invention
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly described below with reference to the accompanying drawings and the description of the embodiments or the prior art, and it is obvious that the following description of the structure of the drawings is only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art. It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention.
It should be understood that although the terms first and second, etc. may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, a first object may be referred to as a second object, and similarly a second object may be referred to as a first object, without departing from the scope of example embodiments of the invention.
It should be understood that for the term "and/or" that may appear herein, it is merely one association relationship that describes an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: three cases of A alone, B alone or both A and B exist; as another example, A, B and/or C may represent the presence of any one of A, B and C or any combination thereof; for the term "/and" that may appear herein, which is descriptive of another associative object relationship, it means that there may be two relationships, e.g., a/and B, it may be expressed that: the two cases of A and B exist independently or simultaneously; in addition, for the character "/" that may appear herein, it is generally indicated that the context associated object is an "or" relationship.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly described below with reference to the accompanying drawings and the description of the embodiments or the prior art, and it is obvious that the following description of the structure of the drawings is only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art. It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention.
It should be understood that although the terms first and second, etc. may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, a first object may be referred to as a second object, and similarly a second object may be referred to as a first object, without departing from the scope of example embodiments of the invention.
It should be understood that for the term "and/or" that may appear herein, it is merely one association relationship that describes an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: three cases of A alone, B alone or both A and B exist; as another example, A, B and/or C may represent the presence of any one of A, B and C or any combination thereof; for the term "/and" that may appear herein, which is descriptive of another associative object relationship, it means that there may be two relationships, e.g., a/and B, it may be expressed that: the two cases of A and B exist independently or simultaneously; in addition, for the character "/" that may appear herein, it is generally indicated that the context associated object is an "or" relationship.
In order to solve the problems in the prior art, as shown in FIGS. 1-2, FIG. 1 is a diagram showing the AI real-time analysis of the present invention
FIG. 2 is a diagram showing an intelligent patrol record according to the present invention; the invention provides a video safety management system for omnibearing intelligent monitoring for solving the problems,
The device is characterized by comprising the following modules:
the camera module is used for acquiring video or photo information of the catering center according to the setting of the user;
The AI analysis module is used for carrying out real-time detection or detection analysis according to the setting of a user;
the intelligent inspection module, the camera module can change the position according to the preset track to obtain more detailed video or photo information;
and the monitoring management module is used for carrying out corresponding statistical analysis according to the selection parameters of the user.
As an embodiment, the system further comprises an intelligent guide rail for use with the camera module.
As a specific embodiment, when the scene is abnormal, such as wearing no mask, smoking, wearing no cap, curing a mouse, and the like, the camera module can move along the guide rail under the instruction of the intelligent control module, so that the camera module can focus the abnormal scene information in a high-definition manner, the eyes of a background analyst can be further identified, and the system performs high-definition evidence collection, so that the warning education on related personnel can be performed later. The intelligent guide rail is arranged in relation to the shooting range of the shooting module, and the constraint conditions of the intelligent guide rail and the shooting range are 1) the secondary full coverage of scenes is realized, namely each scene must appear at least 2 times in all acquired video or image evidences; 2) Each frame of image, which is spatially at a different location, must have a scene intersection with images acquired by other camera modules.
As an embodiment, the system further includes an intelligent control module, capable of sending a control instruction according to the information acquired by the camera module, so as to control the intelligent inspection module to move.
As an embodiment, the AI analysis module implements mask wear detection, cap detection, smoke detection, and mouse detection.
Optionally, the detection is implemented by a deep neural network model, and the deep neural network comprises an input layer, one or more hidden layers and an output layer;
when training, the input layer is used for receiving pictures containing masks, caps, smoking and mice; when the method is used for real-time or non-real-time analysis, the input layer receives image information acquired in real time or a scene image to be analyzed;
Optionally, the hidden layer comprises one or more convolution layers, one or more pooling layers;
optionally, the deep neural network adopts a loss function, and is defined as follows:
N represents the size of a sample data set in the history record, i represents the value of 1-N, and y i represents a label corresponding to a sample x i; /(I) Representing the weight of sample x i at its tag y i, the b vector includes/>And b j,/>Representing the deviation of sample x i at its tag y i, b j representing the deviation at output node j;
optionally, the pooling method is as follows:
xe＝f(weφ(xe-1))；
Where x e represents the output of the current layer, w e represents the weight of the current layer, phi represents the log likelihood loss function, and x e-1 represents the output of the previous layer;
N represents the size of the sample data set contained in the history; yi represents the label value corresponding to the sample feature vector x i; w yi denotes the weight of sample feature vector x i at its label yi, and θ yi denotes the vector angle of sample x i with its corresponding label yi;
the output layer is used for outputting abnormal types, including abnormal types such as mask, cap, smoking, mice and the like;
And training the deep neural network continuously until a preset condition is met, so as to obtain a trained deep neural network model.
As an embodiment, the system further comprises an early warning module, and when the abnormal condition or the irregular condition is detected in real time, the early warning module sends out warning information to related responsible personnel.
The application also provides a video safety management method for omnibearing intelligent monitoring, which is characterized by comprising the following steps:
S1, acquiring information, namely acquiring video or photo information of a catering center according to the setting of a user;
s2, AI analysis step, real-time detection or detection analysis is carried out according to the setting of the user;
s3, an intelligent inspection step, wherein the camera module performs position change according to a preset track so as to obtain more detailed video or photo information;
and S4, monitoring and managing, namely performing corresponding statistical analysis according to the selection parameters of the user.
As an embodiment, the method further comprises: the camera module is matched with the intelligent guide rail for use, and multi-angle information acquisition is completed.
As a specific embodiment, when the scene is abnormal, such as wearing no mask, smoking, wearing no cap, curing a mouse, and the like, the camera module can move along the guide rail under the instruction of the intelligent control module, so that the camera module can focus the abnormal scene information in a high-definition manner, the eyes of a background analyst can be further identified, and the system performs high-definition evidence collection, so that the warning education on related personnel can be performed later. The intelligent guide rail is arranged in relation to the shooting range of the shooting module, and the constraint conditions of the intelligent guide rail and the shooting range are 1) the secondary full coverage of scenes is realized, namely each scene must appear at least 2 times in all acquired video or image evidences; 2) Each frame of image, which is spatially at a different location, must have a scene intersection with images acquired by other camera modules.
As an embodiment, the method further includes sending, by using an intelligent control module, an instruction according to the information obtained by the camera module, so as to control the intelligent inspection module to move.
As an embodiment, the AI analysis step implements mask wear detection, cap detection, smoke detection, and mouse disease detection.
Optionally, the detection is implemented by a deep neural network model, and the deep neural network comprises an input layer, one or more hidden layers and an output layer;
when training, the input layer is used for receiving pictures containing masks, caps, smoking and mice; when the method is used for real-time or non-real-time analysis, the input layer receives image information acquired in real time or a scene image to be analyzed;
Optionally, the hidden layer comprises one or more convolution layers, one or more pooling layers;
optionally, the deep neural network adopts a loss function, and is defined as follows:
N represents the size of a sample data set in the history record, i represents the value of 1-N, and y i represents a label corresponding to a sample x i; /(I) Representing the weight of sample x i at its tag y i, the b vector includes/>And b j,/>Representing the deviation of sample x i at its tag y i, b j representing the deviation at output node j;
optionally, the pooling method is as follows:
xe＝f(weφ(xe-1))；
Where x e represents the output of the current layer, w e represents the weight of the current layer, phi represents the log likelihood loss function, and x e-1 represents the output of the previous layer;
N represents the size of the sample data set contained in the history; yi represents the label value corresponding to the sample feature vector x i; w yi denotes the weight of sample feature vector x i at its label yi, and θ yi denotes the vector angle of sample x i with its corresponding label yi;
the output layer is used for outputting abnormal types, including abnormal types such as mask, cap, smoking, mice and the like;
And training the deep neural network continuously until a preset condition is met, so as to obtain a trained deep neural network model.
As an embodiment, the method further comprises sending a warning message to the relevant responsible person by the early warning module when the nonstandard or abnormal condition is detected in real time.
Furthermore, the invention provides a computer device comprising a memory, a processor and a transceiver in communication connection in turn, wherein the memory is used for storing a computer program, the transceiver is used for receiving and transmitting messages, and the processor is used for reading the computer program and executing any one of the methods.
The present invention provides a computer readable storage medium having instructions stored thereon which, when executed on a computer, perform any of the methods described above.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (3)
1. The video safety management system for omnibearing intelligent monitoring is characterized by comprising the following modules:
the camera module is used for acquiring video or photo information of the catering center according to the setting of the user;
The AI analysis module is used for carrying out real-time detection or detection analysis according to the setting of a user and realizing mask wearing detection, cap detection, smoking detection and mouse disease detection;
The intelligent inspection module enables the camera module to change the position according to a preset track so as to obtain more detailed video or photo information;
The monitoring management module is used for carrying out corresponding statistical analysis according to the selection parameters of the user;
When the scene is abnormal, including no mask, smoking, no hat and mouse, the camera module moves along the guide rail under the instruction of the intelligent control module until the camera module can focus the abnormal scene information in a high definition mode, so that the eyes of background analysts can be conveniently and further identified, and the system performs high definition evidence collection so as to be convenient for subsequent warning education on related personnel;
the video safety management system also comprises an intelligent guide rail matched with the camera module;
the intelligent guide rail is arranged in relation to the shooting range of the shooting module, and the constraint conditions of the intelligent guide rail and the shooting range are as follows:
1) Realizing secondary full coverage of scenes, i.e. each scene must appear at least 2 times in all acquired video or image evidence;
2) Each frame of image at different positions in space must have scene intersection with images acquired by other camera modules;
the detection is realized by adopting a deep neural network model, and the deep neural network comprises an input layer, one or more hidden layers and an output layer;
when training, the input layer is used for receiving pictures containing masks, caps, smoking and mice;
when the method is used for real-time or non-real-time analysis, the input layer receives image information acquired in real time or a scene image to be analyzed;
The hidden layer comprises one or more convolution layers and one or more pooling layers;
The deep neural network adopts a loss function to define as follows:
N represents the size of a data set containing a sample, i represents the values 1-N, yi represents the label corresponding to the sample x i; /(I) Representing the weight of sample x i at its label yi, the b vector includes/>And b j,/>Representing the deviation of sample x i at its label yi, b j representing the deviation at output node j;
the pooling method comprises the following steps:
；
；
wherein, Representing the output of the current layer,/>Representing the weights of the current layer,/>Representing a log-likelihood loss function,An output representing the previous layer; /(I)The vector angle represented by sample x i and its corresponding label yi;
The output layer is used for outputting abnormal types, including mask, cap, smoking and mouse abnormality;
And training the deep neural network continuously until a preset condition is met, so as to obtain a trained deep neural network model.
2. The video safety management system for omnibearing intelligent monitoring according to claim 1, further comprising an intelligent control module capable of sending a control instruction according to the information acquired by the camera module for controlling the intelligent patrol module to move.
3. The video security management system of claim 1, further comprising an early warning module, wherein the early warning module sends out a warning message to the responsible person when an irregular or abnormal condition is detected in real time.
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